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    Many social media posts are accompanied by images or are solely composed of them. Hence, to take full advantage of the information in each tweet, we wish to process not only the text but also the accompanying image content.
    Extracting sentiment information from images is a hard task given the fact that the same image can be interpreted by different people as conveying a different sentiment; even for the same person, a single image might have a different interpretation depending on the occasion that it is observed.
    Nonetheless, several approaches to Image Sentiment Analysis (ISA) have been proposed ((Jindal et al., 2015), (You et al., 2015), (Yuan et al. 2015)) and there are already commercial services that implement ISA, such as Microsoft Cognitive Services (https://azure.microsoft.com/en-us/services/cognitive-services) (although in this case, only face images are processed for extracting sentiment).
    In this WP, we propose to use a three-step approach for ISA. First, we intend to use deep learning approaches (Goodfellow et al., 2016), such as convolutional neural networks or residual nets, to model sentiment in images from pre-labeled databases, such as (Borth et al. Thirdly, we will take advantage of the sentiments in the text that accompanies the images to automatically label a large dataset of tweets and then train the deep learning models using this dataset. ************************************** #MOVES #fct #naturallanguageprocessing #automaticlearning #crowds #socialmedia #socialnetwork #IT #portugal2020 #compete2020 #computerscience #ubi #textanalysis #sentimentanalysis #bigdata

    Many social media posts are accompanied by images or are solely composed of them. Hence, to take full advantage of the information in each tweet, we wish to process not only the text but also the accompanying image content.
Extracting sentiment information from images is a hard task given the fact that the same image can be interpreted by different people as conveying a different sentiment; even for the same person, a single image might have a different interpretation depending on the occasion that it is observed.
Nonetheless, several approaches to Image Sentiment Analysis (ISA) have been proposed ((Jindal et al., 2015), (You et al., 2015), (Yuan et al. 2015)) and there are already commercial services that implement ISA, such as Microsoft Cognitive Services (https://azure.microsoft.com/en-us/services/cognitive-services) (although in this case, only face images are processed for extracting sentiment).
In this WP, we propose to use a three-step approach for ISA. First, we intend to use deep learning approaches (Goodfellow et al., 2016), such as convolutional neural networks or residual nets, to model sentiment in images from pre-labeled databases, such as (Borth et al. Thirdly, we will take advantage of the sentiments in the text that accompanies the images to automatically label a large dataset of tweets and then train the deep learning models using this dataset. ************************************** #MOVES #fct #naturallanguageprocessing #automaticlearning #crowds #socialmedia #socialnetwork #IT #portugal2020 #compete2020 #computerscience #ubi #textanalysis #sentimentanalysis #bigdata
    9 0 22 hours ago

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    An interesting insight about #AI :

    NVIDIA Achieves 4X Speedup on BERT Neural Network
    Recently, a new language representation model called BERT (Bidirectional Encoder Representations from Transformers) was described by Google Research in a paper published on arXiv.
    NVIDIA’s 18.11 containers include optimizations for Transformer models running in PyTorch.
    Converting the model to use mixed precision with V100 Tensor Cores, which computes using FP16 precision and accumulates using FP32, delivered the first speedup of 2.3x.
    The next optimization adds an optimized layer normalization operation called “layer norm” for short, which improves performance by building on the existing cuDNN Batch Normalization primitive, and netted an additional 9% speedup.
    The work done here can be previewed in this public pull request to the BERT github repository.
    #Nvidia #Github #Naturallanguageprocessing #Artificialneuralnetwork #Arxiv
    more details on essentials.news/ai

    An interesting insight about #AI :

NVIDIA Achieves 4X Speedup on BERT Neural Network
Recently, a new language representation model called BERT (Bidirectional Encoder Representations from Transformers) was described by Google Research in a paper published on arXiv.
NVIDIA’s 18.11 containers include optimizations for Transformer models running in PyTorch.
Converting the model to use mixed precision with V100 Tensor Cores, which computes using FP16 precision and accumulates using FP32, delivered the first speedup of 2.3x.
The next optimization adds an optimized layer normalization operation called “layer norm” for short, which improves performance by building on the existing cuDNN Batch Normalization primitive, and netted an additional 9% speedup.
The work done here can be previewed in this public pull request to the BERT github repository.
#Nvidia #Github #Naturallanguageprocessing #Artificialneuralnetwork #Arxiv 
more details on essentials.news/ai
    14 1 13 December, 2018

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    Simple and instructive #AI Article

    A Deep Learning Theory: Global Minima and Over-Parameterization
    Yet, from a theory perspective, why and how SGD finds global minima over the set of non-convex and non-smooth neural networks remains somewhat mysterious.
    Our main finding demonstrates that, for state-of-the-art network architectures such as fully-connected neural networks, convolutional networks (CNN), or residual networks (Resnet), assuming there are n training samples without duplication, as long as the number of parameters is polynomial in n, first-order methods such as SGD can find global optima of the training objective efficiently, that is, with running time only polynomially dependent on the total number of parameters of the network.
    These two theorems together imply that SGD finds global minima on over-parameterized neural networks.
    How does the trained network generalize to test data?
    To properly answer this question, we teamed up with University of Wisconsin–Madison Professor Yingyu Liang to work out a generalization theory for such over-parameterized neural networks trained by SGD.
    #Microsoftresearch #Globaloptimization #Cnn #Naturallanguageprocessing #Artificialneuralnetwork
    more details on essentials.news/ai

    Simple and instructive #AI Article

A Deep Learning Theory: Global Minima and Over-Parameterization
Yet, from a theory perspective, why and how SGD finds global minima over the set of non-convex and non-smooth neural networks remains somewhat mysterious.
Our main finding demonstrates that, for state-of-the-art network architectures such as fully-connected neural networks, convolutional networks (CNN), or residual networks (Resnet), assuming there are n training samples without duplication, as long as the number of parameters is polynomial in n, first-order methods such as SGD can find global optima of the training objective efficiently, that is, with running time only polynomially dependent on the total number of parameters of the network.
These two theorems together imply that SGD finds global minima on over-parameterized neural networks.
How does the trained network generalize to test data?
To properly answer this question, we teamed up with University of Wisconsin–Madison Professor Yingyu Liang to work out a generalization theory for such over-parameterized neural networks trained by SGD.
#Microsoftresearch #Globaloptimization #Cnn #Naturallanguageprocessing #Artificialneuralnetwork 
more details on essentials.news/ai
    3 0 12 December, 2018

    En kısa ifade ile, Blockchain şifrelenmiş işlem takibi sağlayan dağıtık veri kayıt sistemidir. Bir veritabanı değildir çünkü kaydedilen veri bir daha değiştirilemez veya silinemez. Bu özelliğini verilerin biriktirildikleri blokları aynı bir zincir gibi, birbirlerine şifreleme algoritmaları ile bağlayarak saklamasına ve bu zincirin birçok kişiyle dağıtık olarak paylaşılmasına borçludur. Günlük hayattan bir örnek ile daha iyi anlayalım.
    Blockchain’in verileri tutma mantığı aslında bizim bakkalların veresiye defterleri ile biraz benzerlik gösteriyor. Eskiden bakkaldan bir şey almaya giderken evin veresiye defterini de yanımıza alarak giderdik. Bakkaldan alışveriş yapıldığında hem bakkal kendi veresiye defterine yazar hem de biz evin veresiye defterine yazardık. Burada amaç bakkalın bizden habersiz veresiye defterinde değişiklik yapmasını önlemekti. İşte blockchain’de ki dağıtık ifadesi de bu mantığa çok benziyor. İşlemlerin kayıtlı olduğu blok zinciri ağ üzerindeki herkes ile aynı olacak şekilde tutulur. Eğer herhangi biri kendi defterinde diğerlerinin onayı olmadan bir şey eklemeye kalkarsa, diğer defterlerle çatışacağı için ağ dışında kalacaktır.
    Bu anlamda Blockchain bizim değiştirilemez ve manipüle edilemez kayıtlar tutmamızı sağlar. Ve bu teknolojiyi bu kadar büyük yapan asıl olay ise merkezi bir otoriteye ihtiyaç duymamasıdır. Bu işlemlerin deftere kaydı ve ağa yayılması tamamen demokratik bir biçimde ağ üzerindeki bilgisayarlar tarafından yapılır. Ne kadar çok bilgisayar bu ağa katılırsa bu sistemin güvenilirliğini o derece de arttıracaktır.

    #blockchain #blokzinciri #blockchaintechnology #ethereum #bitcoin #datascience #veribilimi #yapayzeka #derinöğrenme #doğaldili ̇şleme #java #phyton #c #r #cpp #javascript #reactnative #angular #web #html #css #artificialintelligence #naturallanguageprocessing #btctürk

    En kısa ifade ile, Blockchain şifrelenmiş işlem takibi sağlayan dağıtık veri kayıt sistemidir. Bir veritabanı değildir çünkü kaydedilen veri bir daha değiştirilemez veya silinemez. Bu özelliğini verilerin biriktirildikleri blokları aynı bir zincir gibi, birbirlerine şifreleme algoritmaları ile bağlayarak saklamasına ve bu zincirin birçok kişiyle dağıtık olarak paylaşılmasına borçludur. Günlük hayattan bir örnek ile daha iyi anlayalım.
Blockchain’in verileri tutma mantığı aslında bizim bakkalların veresiye defterleri ile biraz benzerlik gösteriyor. Eskiden bakkaldan bir şey almaya giderken evin veresiye defterini de yanımıza alarak giderdik. Bakkaldan alışveriş yapıldığında hem bakkal kendi veresiye defterine yazar hem de biz evin veresiye defterine yazardık. Burada amaç bakkalın bizden habersiz veresiye defterinde değişiklik yapmasını önlemekti. İşte blockchain’de ki dağıtık ifadesi de bu mantığa çok benziyor. İşlemlerin kayıtlı olduğu blok zinciri ağ üzerindeki herkes ile aynı olacak şekilde tutulur. Eğer herhangi biri kendi defterinde diğerlerinin onayı olmadan bir şey eklemeye kalkarsa, diğer defterlerle çatışacağı için ağ dışında kalacaktır.
Bu anlamda Blockchain bizim değiştirilemez ve manipüle edilemez kayıtlar tutmamızı sağlar. Ve bu teknolojiyi bu kadar büyük yapan asıl olay ise merkezi bir otoriteye ihtiyaç duymamasıdır. Bu işlemlerin deftere kaydı ve ağa yayılması tamamen demokratik bir biçimde ağ üzerindeki bilgisayarlar tarafından yapılır. Ne kadar çok bilgisayar bu ağa katılırsa bu sistemin güvenilirliğini o derece de arttıracaktır.

#blockchain #blokzinciri #blockchaintechnology #ethereum #bitcoin #datascience #veribilimi #yapayzeka #derinöğrenme #doğaldili̇şleme #java #phyton #c #r #cpp #javascript #reactnative #angular #web #html #css #artificialintelligence #naturallanguageprocessing #btctürk
    37 1 12 December, 2018

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